In composite video systems such as National Television System Committee (NTSC) systems and Phase Alternate Line (PAL) systems, color information is transmitted by a color subcarrier superimposed on a luminance signal. Therefore, the luminance signal and a chrominance signal actually share some frequency bands. As a result, two types of artifacts are commonly seen in composite video: cross-chrominance (cross-chroma) artifacts and cross-luminance (cross-luma) artifacts. The cross-chroma artifacts occur when the luminance signal contains frequency components near the color subcarrier frequency and spurious colors are generated in the picture. The cross-chroma artifacts are commonly called “bleeding” or “rainbow effects”. The cross-luma artifacts occur around edges of highly saturated colors as a continuous series of crawling dots where color information is confused with luminance information. The cross-luma artifacts are commonly called “dot crawl”.
Existing filtering techniques use shimmer detection to identify areas of cross-chroma and cross-luma. Noise detection is conventionally used to control a strength of adaptive noise filtering to improve performance. A noise level is typically estimated either temporally or spatially. Temporal estimations are made from (i) co-located block differences or from residual block energies after motion compensation. Spatial estimations are made from block frequency distributions. Existing techniques can also use motion vectors to detect stationary regions and apply filtering on a block-level where the stationary regions and shimmer are simultaneously detected.
The existing techniques have known failures and limitations resulting in a lack of quality, complexity and expenses. For example, motion compensated noise level measurements are considered reliable. However, motion compensation is expensive both computationally and in terms of memory bandwidth. Therefore, motion compensation noise level measurements are not suitable for many low-end, large volume applications. In another example, spatial (i.e., block frequency distribution) based methods using comparison of measures (i.e., distributions and distributions of co-located measure differences) over a small number of adjacent frames are typically unable to distinguish between high-texture and high noise, particularly in the presence of motion. Furthermore, motion vector analysis is complex. Existing shimmer detection methods are also more complex than is desired for composite noise detection. Existing methods are not bandwidth and computationally efficient for detecting cross-chroma and cross-luma. In particular, no existing method is known to filter a “circular sweep zone plate” correctly due to unique spatio-temporal characteristics. In addition, the existing methods do not explicitly filter differently for cases of strong dot crawl and weak dot crawl.